Curvilinear Data Processing Methods and Verification

Designs for photonic devices on silicon relies on non-Manhattan features such as curves and a wide variety of angles. Reticle Enhancement Techniques (RET) that are commonly used for CMOS manufacturing now are applied to curvilinear data patterns for the same reasons of enhancing pattern fidelity. Common techniques for curvilinear data processing include Manhattanization, jog removal, and jog alignment. We propose a novel method of describing curvilinear shapes in terms of curves reconstructed between control points. Such representation of curvilinear shapes brings many benefits in terms of pattern description (improved fidelity, file compaction), correction and verification. For example, it allows smooth displacements during the design correction procedure for process effects. The conventional correction by biasing each fragment illustrates the curve-based biasing where only the control points have been moved and the corrected shape was then reconstructed by connecting the control points in their new positions by the new curves. This method results in faster computation because there are fewer locations to adjust geometry, easier convergence and intrinsic continuity between edges. It also affords significant reduction of the design file size. Besides processing curvilinear pattern data, verification is also required after any original pattern modifications. Mask Rule Checks (MRC) are considered as standard step in any design data preparation flows, but the conventional MRC algorithms are conceived for Manhattan designs and as such they often result in numerous false errors or even missing errors when applied to photonics or ILT (Inverse Lithography Technology) designs. In addition, MRC for photonic layouts require much more than basic width and space checking. We developed a verification technology compliant with curvilinear layouts. The new MRC technique is also based on curve representation of the original design comparing directly the curves instead of the straight fragments. It permits to have only one error flag per curve instead of multiple errors seen in fragment-by-fragment MRC.